Cycling Segments Multimodal Analysis and Classification Using Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63516820" target="_blank" >RIV/70883521:28140/17:63516820 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/17:00318548 RIV/00216208:11150/17:10367933 RIV/60461373:22340/17:43903861

Výsledek na webu

<a href="http://www.mdpi.com/2076-3417/7/6/581/xml" target="_blank" >http://www.mdpi.com/2076-3417/7/6/581/xml</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app7060581" target="_blank" >10.3390/app7060581</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cycling Segments Multimodal Analysis and Classification Using Neural Networks

Popis výsledku v původním jazyce

This paper presents methodology for the processing of GPS and heart rate signals acquired during long-term physical activities. The data analysed include geo-positioning and heart rate multichannel signals recorded for 272.2 h of cycling across the Andes mountains over a 5694-km long expedition. The proposed computational methods include multimodal data de-noising, visualization, and analysis in order to determine specific biomedical features. The results include the correspondence between the heart rate and slope for downhill and uphill cycling and the mean heart rate evolution on flat segments: a regression coefficient of -0.014 bpm/km related to altitude. The classification accuracy of selected cycling features by neural networks, support vector machine, and k-nearest neighbours methods is between 91.3% and 98.6%. The proposed methods allow the analysis of data during physical activities, enabling an efficient human-machine interaction.

Název v anglickém jazyce

Cycling Segments Multimodal Analysis and Classification Using Neural Networks

Popis výsledku anglicky

This paper presents methodology for the processing of GPS and heart rate signals acquired during long-term physical activities. The data analysed include geo-positioning and heart rate multichannel signals recorded for 272.2 h of cycling across the Andes mountains over a 5694-km long expedition. The proposed computational methods include multimodal data de-noising, visualization, and analysis in order to determine specific biomedical features. The results include the correspondence between the heart rate and slope for downhill and uphill cycling and the mean heart rate evolution on flat segments: a regression coefficient of -0.014 bpm/km related to altitude. The classification accuracy of selected cycling features by neural networks, support vector machine, and k-nearest neighbours methods is between 91.3% and 98.6%. The proposed methods allow the analysis of data during physical activities, enabling an efficient human-machine interaction.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Sciences-Basel

ISSN

2076-3417

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

000404449800057

EID výsledku v databázi Scopus

2-s2.0-85020253439